Fully automated quantification of intraretinal cysts in 3D optical coherence tomography

F.G. Venhuizen, M.J.J.P. van Grinsven, B. van Ginneken, C.B. Hoyng, T. Theelen and C.I. Sánchez

in: Association for Research in Vision and Ophthalmology, 2016


Purpose: In age-related macular degeneration (AMD) the presence of intraretinal cysts is an important prognostic biomarker. Optical coherence tomography imaging has shown to be capable of accurately visualizing and characterizing the three-dimensional shape and extent of cysts. The detection and quantification of intraretinal cysts is highly beneficial for the prediction of treatment outcome and the assessment of the treatment progression. To aid the clinician with quantified information regarding cysts, we developed a fully automated system capable of detecting and delineating intraretinal cysts in 3D optical coherence tomography images.Methods: For this study a total of 30 OCT volumes acquired using four different brand OCT scanners were provided by the OPTIMA cyst segmentation challenge, containing a wide variety of retinal cysts together with manual cyst delineations. A pixel classifier based on a multiscale convolutional neural network (CNN) was developed to predict if an image pixel belongs to a cyst or to the background by considering a small neighborhood around the pixel of interest. The CNN follows a two stage approach, where in the first stage, multiple CNN's are used in parallel to obtain a segmentation at different image scales. In the second stage, the individual segmentations are merged, combining local information obtained with the lower scale network with contextual information obtained from the higher scale networks. After providing the neural network with enough training samples, the network can automatically detect and segment cysts in OCT volumes. The obtained segmentations were compared to manual delineations made by two experienced human graders. Results: The spatial overlap agreement on the obtained volumes, measured by the Dice similarity coefficient, between the manual delineations and the software output was 0.55, which is substantial considering the difficulty of the task. In addition, the quantification time was reduced dramatically, and takes only a few seconds for a complete OCT volume. Conclusions: An image analysis algorithm for the automatic quantification of intraretinal cysts in OCT images was developed. The proposed algorithm is able to detect and quantify the three dimensional shape and extent of cysts in a fast and reproducible manner, allowing accurate assessment of disease progression and treatment outcome.